Jason D Morgenstern1, Laura C Rosella2,3,4,5, Mark J Daley5,6,7,8,9, Vivek Goel2,3, Holger J Schünemann1,10, Thomas Piggott11. 1. Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Ontario, Canada. 2. Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada. 3. Institute for Clinical Evaluative Sciences, Toronto, Ontario, Canada. 4. Public Health Ontario, Toronto, Ontario, Canada. 5. Vector Institute, Toronto, Ontario, Canada. 6. Department of Computer Science, Western University, London, Ontario, Canada. 7. Department of Biology, Western University, London, Ontario, Canada. 8. Department of Actuarial Sciences and Statistics, Western University, London, Ontario, Canada. 9. Brain and Mind Institute, Western University, London, Ontario, Canada. 10. Department of Medicine, McMaster University, Hamilton, Ontario, Canada. 11. Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Ontario, Canada. piggott@mcmaster.ca.
Abstract
BACKGROUND: Our objective was to determine the impacts of artificial intelligence (AI) on public health practice. METHODS: We used a fundamental qualitative descriptive study design, enrolling 15 experts in public health and AI from June 2018 until July 2019 who worked in North America and Asia. We conducted in-depth semi-structured interviews, iteratively coded the resulting transcripts, and analyzed the results thematically. RESULTS: We developed 137 codes, from which nine themes emerged. The themes included opportunities such as leveraging big data and improving interventions; barriers to adoption such as confusion regarding AI's applicability, limited capacity, and poor data quality; and risks such as propagation of bias, exacerbation of inequity, hype, and poor regulation. CONCLUSIONS: Experts are cautiously optimistic about AI's impacts on public health practice, particularly for improving disease surveillance. However, they perceived substantial barriers, such as a lack of available expertise, and risks, including inadequate regulation. Therefore, investment and research into AI for public health practice would likely be beneficial. However, increased access to high-quality data, research and education regarding the limitations of AI, and development of rigorous regulation are necessary to realize these benefits.
BACKGROUND: Our objective was to determine the impacts of artificial intelligence (AI) on public health practice. METHODS: We used a fundamental qualitative descriptive study design, enrolling 15 experts in public health and AI from June 2018 until July 2019 who worked in North America and Asia. We conducted in-depth semi-structured interviews, iteratively coded the resulting transcripts, and analyzed the results thematically. RESULTS: We developed 137 codes, from which nine themes emerged. The themes included opportunities such as leveraging big data and improving interventions; barriers to adoption such as confusion regarding AI's applicability, limited capacity, and poor data quality; and risks such as propagation of bias, exacerbation of inequity, hype, and poor regulation. CONCLUSIONS: Experts are cautiously optimistic about AI's impacts on public health practice, particularly for improving disease surveillance. However, they perceived substantial barriers, such as a lack of available expertise, and risks, including inadequate regulation. Therefore, investment and research into AI for public health practice would likely be beneficial. However, increased access to high-quality data, research and education regarding the limitations of AI, and development of rigorous regulation are necessary to realize these benefits.
Entities:
Keywords:
Big data; Community medicine; Epidemiology; Machine learning; Population health; Preventive medicine; Qualitative
Authors: Douglas G Manuel; Meltem Tuna; Carol Bennett; Deirdre Hennessy; Laura Rosella; Claudia Sanmartin; Jack V Tu; Richard Perez; Stacey Fisher; Monica Taljaard Journal: CMAJ Date: 2018-07-23 Impact factor: 8.262
Authors: Isaac I Bogoch; Oliver J Brady; Moritz U G Kraemer; Matthew German; Marisa I Creatore; Manisha A Kulkarni; John S Brownstein; Sumiko R Mekaru; Simon I Hay; Emily Groot; Alexander Watts; Kamran Khan Journal: Lancet Date: 2016-01-15 Impact factor: 79.321
Authors: Elaine Holmes; Ruey Leng Loo; Jeremiah Stamler; Magda Bictash; Ivan K S Yap; Queenie Chan; Tim Ebbels; Maria De Iorio; Ian J Brown; Kirill A Veselkov; Martha L Daviglus; Hugo Kesteloot; Hirotsugu Ueshima; Liancheng Zhao; Jeremy K Nicholson; Paul Elliott Journal: Nature Date: 2008-04-20 Impact factor: 49.962
Authors: Jenny Moberg; Andrew D Oxman; Sarah Rosenbaum; Holger J Schünemann; Gordon Guyatt; Signe Flottorp; Claire Glenton; Simon Lewin; Angela Morelli; Gabriel Rada; Pablo Alonso-Coello Journal: Health Res Policy Syst Date: 2018-05-29
Authors: Daniel Capurro; Kate Cole; Maria I Echavarría; Jonathan Joe; Tina Neogi; Anne M Turner Journal: J Med Internet Res Date: 2014-03-14 Impact factor: 5.428